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Introduction: Ischemic heart disease is a leading cause of death worldwide, and its importance is increasing with the aging population. The aim of this study was to evaluate the accuracy of SurvTrace, a survival analysis model using the Transformer—a state-of-the-art deep learning method—for predicting recurrent cardiovascular events and stratifying high-risk patients. The model's performance was compared to that of a conventional scoring system utilizing real-world data from cardiovascular patients. Methods: This study consecutively enrolled patients who underwent percutaneous coronary intervention (PCI) at the Department of Cardiovascular Medicine, University of Tokyo Hospital, between 2005 and 2019. Each patient's initial PCI at our hospital was designated as the index procedure, and a composite of major adverse cardiovascular events (MACE) was monitored for up to two years post-index event. Data regarding patient background, clinical presentation, medical history, medications, and perioperative complications were collected to predict MACE. The performance of two models—a conventional scoring system proposed by Wilson et al. and the Transformer-based model SurvTrace—was evaluated using Harrell's c-index, Kaplan–Meier curves, and log-rank tests. Results: A total of 3938 cases were included in the study, with 394 used as the test dataset and the remaining 3544 used for model training. SurvTrace exhibited a mean c-index of 0.72 (95% confidence intervals (CI): 0.69–0.76), which indicated higher prognostic accuracy compared with the conventional scoring system's 0.64 (95% CI: 0.64–0.64). Moreover, SurvTrace demonstrated superior risk stratification ability, effectively distinguishing between the high-risk group and other risk categories in terms of event occurrence. In contrast, the conventional system only showed a significant difference between the low-risk and high-risk groups. Conclusion: This study based on real-world cardiovascular patient data underscores the potential of the Transformer-based survival analysis model, SurvTrace, for predicting recurrent cardiovascular events and stratifying high-risk patients. [ABSTRACT FROM AUTHOR]

Right ventricular failure (RVF) is a serious complication after left ventricular assist device (LVAD) implantation. In this report, a case of RVF that developed over two years after LVAD implantation is presented. The patient was a 12-year-old male with dilated phase of hypertrophic cardiomyopathy. He had no risk factors for early or late-onset RVF. However, his right ventricular function worsened after he developed ventricular arrhythmia (VA), and right ventricular dysfunction became exacerbated with an increasing frequency of VAs. He also developed moderate aortic insufficiency (AI), which became severe. Two years after implantation, he was admitted for treatment of recurrent ventricular tachycardia and became inotropic-dependent during hospitalization. Finally, he underwent successful heart transplantation 2 years and 9 months after LVAD implantation. This case suggests that vicious cycle of RV dysfunction, recurrent VAs and severe AI could lead to RVF in patients without known risk factors for RVF, even long after LVAD implantation. This report shows a progressive right ventricular failure (RVF) two years after left ventricular assist device (LVAD) implantation. Although the patient had no known risk factor, vicious circle of RV dysfunction, ventricular arrhythmias (VAs) and aortic insufficiency (AI) lead to RVF. Patients with LVAD as destination therapy will increase and require long-term LVAD management. We should recognize that these patients could develop RVF even years after LVAD implantation in association with VAs and AI. [ABSTRACT FROM AUTHOR]

Background: Marfan syndrome (MFS) is an inherited disorder caused by mutations in the FBN1 gene encoding fibrillin-1, a matrix component of extracellular microfibrils. The main cause of morbidity and mortality in MFS is thoracic aortic aneurysm and dissection, but the underlying mechanisms remain undetermined., Methods: To elucidate the role of endothelial XOR (xanthine oxidoreductase)-derived reactive oxygen species in aortic aneurysm progression, we inhibited in vivo function of XOR either by endothelial cell (EC)-specific disruption of the Xdh gene or by systemic administration of an XOR inhibitor febuxostat in MFS mice harboring the Fbn1 missense mutation p.(Cys1041Gly). We assessed the aberrant activation of mechanosensitive signaling in the ascending aorta of Fbn1 C1041G/+ mice. Further analysis of human aortic ECs investigated the mechanisms by which mechanical stress upregulates XOR expression., Results: We found a significant increase in reactive oxygen species generation in the ascending aorta of patients with MFS and Fbn1 C1041G/+ mice, which was associated with a significant increase in protein expression and enzymatic activity of XOR protein in aortic ECs. Genetic disruption of Xdh in ECs or treatment with febuxostat significantly suppressed aortic aneurysm progression and improved perivascular infiltration of macrophages. Mechanistically, mechanosensitive signaling involving FAK (focal adhesion kinase)-p38 MAPK (p38 mitogen-activated protein kinase) and Egr-1 (early growth response-1) was aberrantly activated in the ascending aorta of Fbn1 C1041G/+ mice, and mechanical stress on human aortic ECs upregulated XOR expression through Egr-1 upregulation. Consistently, EC-specific knockout of XOR or systemic administration of febuxostat in Fbn1 C1041G/+ mice suppressed reactive oxygen species generation, FAK-p38 MAPK activation, and Egr-1 upregulation., Conclusions: Aberrant activation of mechanosensitive signaling in vascular ECs triggered endothelial XOR activation and reactive oxygen species generation, which contributes to the progression of aortic aneurysms in MFS. These findings highlight a drug repositioning approach using a uric acid-lowering drug febuxostat as a potential therapy for MFS.

Pulmonary hypertension (PH) is a serious prognostic complication in patients with systemic sclerosis (SSc). Deep learning models can be applied to detect PH in the chest X-ray images of these patients. The aim of the study was to investigate the performance and prognostic implications of a deep learning algorithm for the diagnosis of PH in SSc patients using chest X-ray images.Chest X-ray images were acquired from 230 SSc patients with suspected PH who underwent chest X-ray and right heart catheterization (RHC). A convolutional neural network was trained to identify the data of patients with PH (mean pulmonary arterial pressure > 20 mmHg). Kaplan-Meier analysis was used to evaluate survival. The area under the receiver operating characteristic curve (AUC) obtained with the deep learning algorithm was 0.826 while the AUC obtained with cardiologist assessments of the same images was 0.804. The 5-year prognosis was 83.4% in patients with PH detected by RHC, and 85% in those with PH detected by the model.The deep learning model developed in this study can detect PH from the chest X-ray data of SSc patients. The prognostic accuracy of the model was demonstrated as well.

The generalization of deep neural network algorithms to a broader population is an important challenge in the medical field. We aimed to apply self-supervised learning using masked autoencoders (MAEs) to improve the performance of the 12-lead electrocardiography (ECG) analysis model using limited ECG data. We pretrained Vision Transformer (ViT) models by reconstructing the masked ECG data with MAE. We fine-tuned this MAE-based ECG pretrained model on ECG-echocardiography data from The University of Tokyo Hospital (UTokyo) for the detection of left ventricular systolic dysfunction (LVSD), and then evaluated it using multi-center external validation data from seven institutions, employing the area under the receiver operating characteristic curve (AUROC) for assessment. We included 38,245 ECG-echocardiography pairs from UTokyo and 229,439 pairs from all institutions. The performances of MAE-based ECG models pretrained using ECG data from UTokyo were significantly higher than that of other Deep Neural Network models across all external validation cohorts (AUROC, 0.913-0.962 for LVSD, p < 0.001). Moreover, we also found improvements for the MAE-based ECG analysis model depending on the model capacity and the amount of training data. Additionally, the MAE-based ECG analysis model maintained high performance even on the ECG benchmark dataset (PTB-XL). Our proposed method developed high performance MAE-based ECG analysis models using limited ECG data., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Sawano et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Epidemiological evidence of increased risks of cancer in heart failure (HF) patients and HF in cancer patients has suggested close relationships between the pathogenesis of both diseases. Indeed, HF and cancer share common risk factors, including aging and unhealthy lifestyles, and underlying mechanisms, including activation of the sympathetic nervous system and renin-angiotensin-aldosterone system, chronic inflammation, and clonal hematopoiesis of indeterminate potential. Mechanistically, HF accelerates cancer development and progression via secreted factors, so-called cardiokines, and epigenetic remodeling of bone marrow cells into an immunosuppressive phenotype. Reciprocally, cancer promotes HF via cachexia-related wasting and metabolic remodeling in the heart, and possibly via cancer-derived extracellular vesicles influencing myocardial structure and function. The novel concept of the "heart-cancer axis" will help in our understanding of the shared and reciprocal relationships between HF and cancer, and provide innovative diagnostic and therapeutic approaches for both diseases.

Aims: The black box nature of artificial intelligence (AI) hinders the development of interpretable AI models that are applicable in clinical practice. We aimed to develop an AI model for classifying patients of reduced left ventricular ejection fraction (LVEF) from 12-lead electrocardiograms (ECG) with the decision-interpretability., Methods and Results: We acquired paired ECG and echocardiography datasets from the central and co-operative institutions. For the central institution dataset, a random forest model was trained to identify patients with reduced LVEF among 29 907 ECGs. Shapley additive explanations were applied to 7196 ECGs. To extract the model's decision criteria, the calculated Shapley additive explanations values were clustered for 192 non-paced rhythm patients in which reduced LVEF was predicted. Although the extracted criteria were different for each cluster, these criteria generally comprised a combination of six ECG findings: negative T-wave inversion in I/V5-6 leads, low voltage in I/II/V4-6 leads, Q wave in V3-6 leads, ventricular activation time prolongation in I/V5-6 leads, S-wave prolongation in V2-3 leads, and corrected QT interval prolongation. Similarly, for the co-operative institution dataset, the extracted criteria comprised a combination of the same six ECG findings. Furthermore, the accuracy of seven cardiologists' ECG readings improved significantly after watching a video explaining the interpretation of these criteria (before, 62.9% ± 3.9% vs. after, 73.9% ± 2.4%; P = 0.02)., Conclusion: We visually interpreted the model's decision criteria to evaluate its validity, thereby developing a model that provided the decision-interpretability required for clinical application., Competing Interests: Conflict of interest: None declared., (© The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology.)